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 We are looking for students finishing their MSc with a solid background in computer vision and machine learning, particularly in deep learning with strong PyTorch coding skills.
 Interns work on research topics, typically for 6 months (usually spring and summer), resulting for a great part in paper submissions to top-tier conferences. Some trainees go on to do a PhD thesis in the lab.
 
+# Open Internship Proposals
+
+We currently have four exciting internship opportunities for MSc students!  
+
+### How to Apply  
+Send an email to the supervisors (one email per application) with the following:  
+- A cover letter explaining your interest and qualifications for the topic.  
+- Your CV/resume.  
+- Transcripts of your grades from last year (and this year, if available).  
+
+**Available Topics:**  
+
+**Universal 2D-3D Transformer**  
+*Supervisors*: [Tuan-Hung Vu](mailto:tuan-hung.vu@valeo.com), [Gilles Puy](mailto:gilles.puy@valeo.com), [Spyros Gidaris](mailto:spyros.gidaris@valeo.com)  
+This project aims to develop a novel transformer architecture capable of processing 2D and 3D data simultaneously, probing synergistic multi-modal representations between imagery and LiDAR data.  
+
+**Learning from One Continuous Long-Range Video Stream**  
+*Supervisors*: [Shashanka Venkataramanan](mailto:shashanka.venkataramanan@valeo.com), [Andrei Bursuc](mailto:andrei.bursuc@valeo.com)  
+This internship involves building a video understanding model inspired by human episodic memory to learn continuously from long-range streams. It includes exploring continual learning, memory integration, and advanced pretraining techniques using real-world video datasets.  
+
+**Scenario Generation for Robust Autonomous Driving using Diffusion Models**  
+*Supervisors*: [Yuan Yin](mailto:yuan.yin@valeo.com), [Yihong Xu](mailto:yihong.xu@valeo.com)  
+This internship explores using diffusion models to generate driving scenarios, focusing on map and trajectory creation. The goal is to develop robust, vector-based maps and diverse vehicle behaviors to enhance motion forecasting and planning.  
+
+**Object Generation from Range Images**  
+*Supervisors*: [Nermin Samet](mailto:nermin.samet@valeo.com), [Victor Besnier](mailto:victor.besnier@valeo.com)  
+This project focuses on generating LiDAR point cloud objects by leveraging pre-trained diffusion models on range image representations. The goal is to improve the controllability of LiDAR object generation in a computationally efficient way.  
+
+
 # Alumni interns and visiting students
 
 {% assign sorted_interns = site.data.interns %}